In many types of computer systems, and in particular in sophisticated electronic commerce (e-commerce) sites, it is common for information provided to a user to be tailored to the user. In an e-commerce environment typically such personalized service is made available to users by server systems (often web servers) that are able to capture information to “learn” about a specific user. Based on this information about the user, an e-commerce system is able to provide catalogs, products, services and other information, all of which is targeted to that user. Such system is said to be personalized as the information is related to the system user.
At the heart of personalized e-commerce systems are recommendation technologies. There are many technologies available using differing approaches to presenting tailored information (collectively referred to as recommendations) to users. These include approaches based on rules, collaborative filtering, neural networks, data mining, and other artificial intelligence technologies. Such techniques for implementing a personalized system can be classified in two categories—those that are rule-based (declarative systems) and other non-rule-based (empirical systems).
Rules are declarative and are typically defined by a system author based on information provided by an expert in the knowledge domain pertaining to the e-commerce site in question. The other, non-rule-based, approaches may be described as falling within a “black-box” type of technology. Such approaches are empirical as they rely on data that is typically derived from traits and characteristics of the site users. A system based on such an approach is designed to provide recommendations without relying on expert knowledge of a relevant domain. Rather than an expert setting out the characteristics of the system based on domain knowledge, the system adapts to the domain of the site based on user interaction and other data available to the empirical recommendation system.
In the prior art, site designers typically choose between these two types of technologies when determining how to provide personalization in site designs. However, there are pros and cons associated with the technologies in both of the two categories. Rules are controllable and the results are deterministic. On the other hand, initial definition of the rules requires domain knowledge. Maintaining and updating the rules requires continued investment and expertise. Such an approach tends not to be adaptive or flexible.
The empirical type of personalization technology does not require the same degree of set-up and ongoing maintenance of domain knowledge. However, a system based on this type of technology is not as controllable, nor is the result deterministic. In the e-commerce context, such a system it is also likely to reach a result that, while potentially good for the user (or consumer) because it is based on recorded consumer characteristics, may not be in the best interest of the merchant or retailer.
Because systems are developed using a particular selected recommendation technology, where a particular approach is found not to meet the needs of a site, switching from that technology to another usually requires a substantial rewrite of personalization system interface on the site.
It is therefore desirable to have a recommendation system that may be used for e-commerce personalization that is able to utilize the strengths of both rule-based technologies and of empirical or non-rule-based technologies.